Manager, Data Engineering

TAG - The Aspen Group•Chicago, IL

4d•$160,000 - $190,000•Onsite

About The Position

The Manager, Data Engineering leads the design, delivery, and reliability of TAG's enterprise data platform across our multi-brand environment. It's a hands-on leadership role covering people management, architecture, platform engineering, and operations. The right person can lead a team of Data Engineers and still do the work themselves — technical design, code reviews, incident management, platform modernization, and engineering standards. They'll also drive AI-assisted development, automation, and cloud cost optimization to keep the team productive and the platform scalable.

Requirements

Bachelor’s degree in computer science, Engineering, Information Systems, or related field.
7+ years of Data Engineering experience with modern cloud data platforms.
3+ years of leadership experience managing Data Engineering teams.
Strong expertise in SQL, Python, dbt, and Airflow/Cloud Composer.
Hands-on experience with Google Cloud Platform, including BigQuery, GCS, Pub/Sub, Dataflow, Cloud Functions, and Vertex AI.
Experience with Infrastructure as Code (Terraform) and CI/CD automation.
Strong understanding of data warehousing, semantic modeling, data quality, and governance across batch, streaming, and event-driven architectures.
Strong communication and stakeholder management skills.
Ability to translate business requirements into scalable technical solutions.
Experience leading cross-functional initiatives and influencing technical direction.
Track record of building strong teams and a solid engineering culture.

Nice To Haves

GCP Professional Data Engineer Certification a plus.

Responsibilities

Lead, mentor, and grow a team of Data Engineers across multiple domains.
Run sprint planning, backlog prioritization, and delivery.
Balance strategic work, operational support, and technical debt.
Partner with business, analytics, and technology leaders to align priorities.
Own the architecture and delivery of scalable data pipelines, semantic models, and data products.
Set standards for data modeling, integration, and platform architecture across dbt, Airflow/Cloud Composer, BigQuery, Terraform, and CI/CD.
Lead design reviews, evaluate new technologies, and drive platform modernization.
Lead AI-assisted development (Claude AI/Gemini Enterprise) across code reviews, documentation, testing, and automation.
Set standards and governance for responsible use of AI-generated code.
Own data platform reliability, monitoring, incident response, and root cause analysis.
Establish SLAs, operational metrics, and on-call processes.
Ensure compliance with data governance, security, privacy, and access management.
Optimize performance, scalability, and cost across GCP, including BigQuery consumption, storage, and slot utilization.